A MV Portfolio Investment Strategy Model based on Economic Indicators
DOI: 10.23977/ferm.2022.050404 | Downloads: 12 | Views: 697
Author(s)
Meilin Ouyang 1
Affiliation(s)
1 College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, 400074, China
Corresponding Author
Meilin OuyangABSTRACT
Market traders buy and sell volatile assets frequently, with a goal to maximize their total return. we conduct a single factor sensitivity analysis on transaction costs, commissions, for our model. Firstly, keeping the output result of the portfolio strategy model unchanged, we solve the reference value range of commissions. Secondly, we use ablation study to obtain the relative sensitivity of commission of gold and bitcoin to the portfolio strategy model. The results show that the reference value range of gold commission and bitcoin commission are [0.96%, 7.22%] and [1.89%, 4.36%] respectively, and the model is more sensitive to the transaction costs of bitcoin than gold. we communicate with traders by memorandum about the structure of our model, the method of use, the robustness of the model, the solution results and gave suggestions on how to use the model.
KEYWORDS
LSTM, Quantitative trading, Portfolio Optimization, MV, Portfolio InvestmentCITE THIS PAPER
Meilin Ouyang, A MV Portfolio Investment Strategy Model based on Economic Indicators. Financial Engineering and Risk Management (2022) Vol. 5: 22-29. DOI: http://dx.doi.org/10.23977/ferm.2022.050404.
REFERENCES
[1] Jansen, Maarten. "Wavelet tresholding and noise reduction." (2000).
[2] Neely, Christopher J., et al. "Forecasting the equity risk premium: the role of technical indicators." Management science 60.7 (2014): 1772-1791.
[3] Ta, Van-Dai, Chuanming Liu, and Direselign Addis Tadesse. "Portfolio optimizationbased stock prediction using long-short term memory network in quantitative trading." Applied Sciences 10.2 (2020): 437.
[4] Ramadhan, Muhammad Murtadha, et al. "Parameter tuning in random forest based on gridsearch method for gender classification based on voice frequency." DEStech Transactions on Computer Science and Engineering 10 (2017).
[5] Akarim, Yasemin Deniz, and Serafettin Sevim. "The impact of mean reversion model on portfolio investment strategies: Empirical evidence from emerging markets." Economic Modelling 31 (2013): 453-459.
[6] Yao, Siyu, Linkai Luo, and Hong Peng. "High-frequency stock trend forecast using LSTM model." 2018 13th International Conference on Computer Science Education (ICCSE). IEEE, 2018.
[7] Roondiwala, Murtaza, Harshal Patel, and Shraddha Varma. "Predicting stock prices using LSTM." International Journal of Science and Research (IJSR) 6.4 (2017): 1754-1756.
[8] Yan, Yingying, and Daguang Yang. "A stock trend forecast algorithm based on deep neural networks." Scientific Programming 2021 (2021).
[9] Liu, Yang, et al. "Adaptive quantitative trading: An imitative deep reinforcement learning approach." Proceedings of the AAAI conference on artificial intelligence. Vol. 34. No. 02. 2020.
[10] Zou, Zhichao, and Zihao Qu. "Using LSTM in Stock prediction and Quantitative Trading." CS230: Deep Learning, Winter (2020).
Downloads: | 17889 |
---|---|
Visits: | 348864 |
Sponsors, Associates, and Links
-
Information Systems and Economics
-
Accounting, Auditing and Finance
-
Industrial Engineering and Innovation Management
-
Tourism Management and Technology Economy
-
Journal of Computational and Financial Econometrics
-
Accounting and Corporate Management
-
Social Security and Administration Management
-
Population, Resources & Environmental Economics
-
Statistics & Quantitative Economics
-
Agricultural & Forestry Economics and Management
-
Social Medicine and Health Management
-
Land Resource Management
-
Information, Library and Archival Science
-
Journal of Human Resource Development
-
Manufacturing and Service Operations Management
-
Operational Research and Cybernetics